A novel approach for assessing reliability of ICA for FMRI analysis

Damaraju

et al. 2019

Preprint

Self Cite

Studying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach.First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.

Section: Real Dataset and Preprocessingmentioning

confidence: 99%

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Damaraju

et al. 2019

Preprint

Self Cite

“…Infomax ICA was repeated 100 times. The estimated components from all runs were clustered together, and the centrotype of each cluster was selected as the "best run" as part of the ICASSO framework, which was used for further analyses (Calhoun & Adali, 2002;Calhoun, Liu, & Adali, 2009;Correa, Adali, & Calhoun, 2007;Du, Ma, Fu, Calhoun, & Adali, 2014;Himberg, Hyvarinen, & Esposito, 2004;Ma et al, 2011). Sixty-five cortical and subcortical hICNs were selected and categorized into nine FDs based on their anatomical and common functional properties, and their relationships (spatiotemporal similarity) with independent components obtained from low-order spatial ICA (Figure 2b).…”

Section: Hicns Extractionmentioning

confidence: 99%

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time‐varying brain function

Damaraju

et al. 2018

Human Brain Mapping

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The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FD), as well as temporal dynamics within and between FD. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g. the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variation provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

“…Infomax was chosen as the ICA algorithm because it has been widely used and compares favorability with other algorithms (Correa et al, 2007b;Correa et al, 2005). Infomax ICA was repeated 100 times using ICASSO framework (Himberg et al, 2004), and the 'best run' was selected to obtain a stable and reliable estimation (Calhoun and Adali, 2002;Calhoun et al, 2009;Correa et al, 2007a;Du et al, 2014;Ma et al, 2011). Sixty-five cortical and subcortical hICNs were selected and categorized into nine FDs based on their anatomical and common functional properties, and their relationships (spatiotemporal similarity) with independent components obtained from low-order spatial ICA (Figure 2.B).…”

Section: High-order Intrinsic Connectivity Network (Hicns) Extractionmentioning

confidence: 99%

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function

Damarajua

et al. 2018

Preprint

The analysis of time-varying connectivity in functional magnetic resonance imaging (fMRI) has become an important part of ongoing neuroscience discussions. The majority of such studies have focused primarily on temporal variations of functional connectivity among fixed regions of interest (ROIs) or networks of the brain. However, the brain reorganizes its activity on both temporal and spatial scales.Thus an approach which captures time-varying characteristics of brain functional organizations is essential to improving our understanding of brain function. Here, we propose an approach that uses blood oxygenation-level dependent (BOLD) signal to capture spatiotemporal variations of the functional domains of the brain. The approach is based on the well-accepted assumption that the brain can be modeled as a hierarchical functional architecture with different levels of granularity, and as we move to lower levels of this architecture, the complexity associated with each element of this hierarchy decreases.In other words, lower levels have less dynamic behavior and higher functional homogeneity, and there is a level at which its elements can be approximated as "functional units". A functional unit is a pattern of regions with very similar functional activity over time. At the macro-scale, we suggest high-order intrinsic connectivity networks (hICNs) obtained from a high-order spatial independent component analysis (ICA) provide a way to approximate functional units. We consider hICNs to comprise functional domains, elements of a higher level of the brain functional hierarchy which are being investigated in this study. Focusing on time-varying characteristics of functional domains, our approach captures the unique information of each functional domain at every timepoint and has the capacity to examine spatiotemporal variations of the functional domains up to the maximum temporal and spatial resolutions that exist in the data. Here, k-means clustering was deployed to summarize time-varying characteristics of each functional domain into a set of representatives called spatial domain states. Furthermore, the functional connectivity between domains was examined to identify functional modules. Each functional module contains states of functional domains with higher connectivity with each other than with other states. Our proposed approach was further evaluated using a multi-site dataset of resting-state BOLD/fMRI data collected from . CC-BY-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/391094 doi: bioRxiv preprint first posted online Aug. 13, 2018; 3 160 healthy controls and 149 patients with schizophrenia (SZ). The findings demonstrate that functional domains are spatially fluid over time. Results highlight a negative relationship between the default mode domain and the attention domain in functional module 3. The ...